Choosing Data Augmentation smartly for different application
In fact, choosing smartly the values of the image augmentation can help the performance of your system. Where I work we developed an object detector for cars. We had the following image augmentation parameters:


Apect ratio distorsion: it changed the cars dimensions
Additive noise: it blurred the image
Change colorspace: change the cars colors
Saturation change: brightness
Rotation: rotate image axis
Flipping (horizontally): mirror effect
Zooming: zoom in/out of image


At first we just combined all the effects (within a certain range of change per effect) randomly in each augmented image. But then we did an study for our use case and we found that our model performed better with: no aspect ratio deformation and no change in color space. After giving it a bit of a thought it really made sense for our use case because it is a detector for a very specific type of car. 

However, although the improvements accomplished by optimizing the parameters of image augmentation boosted the performance of the model, the improvements were not huge, so in the end it did not really worth the effort. To give some numbers it improved $5\%$ in our custom test metrics, and we were expecting something around $10\%$ so from that moment on we left the specific image augmentation parameters but stopped thinking too much about it.

So, in conclussion, does it helps? yes, how much? well, that would depend on the use case. For us, it turned out it did not improve as much as we wanted.

Hope this helps :)